File size: 2,700 Bytes
8f1213e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | """Script 05: Train all baselines for comparison.
Three baselines, all on the same train/val/test splits as the Proposed model:
- Baseline 1: TF-IDF + LogReg (text only, overall 3-class)
- Baseline 2: BERT-overall fine-tune (text only, overall 3-class)
- Baseline 3: BERT-ACSA (no meta) (text only, per-aspect 3-class)
^- this is the key ablation for the paper:
Baseline 3 vs Proposed isolates the value of
metadata fusion.
"""
import argparse
import json
import sys
from pathlib import Path
import pandas as pd
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from src.utils import setup_logging
from src import config as cfg
from src.baselines import train_tfidf_baseline
from src.trainer import train_bert_overall, train_acsa
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--skip_tfidf", action="store_true")
parser.add_argument("--skip_bert_overall", action="store_true")
parser.add_argument("--skip_acsa_no_meta", action="store_true")
parser.add_argument("--epochs", type=int, default=cfg.DEFAULT_EPOCHS)
parser.add_argument("--batch_size", type=int, default=cfg.DEFAULT_BATCH_SIZE)
parser.add_argument("--bert_name", default=cfg.BERT_MODEL_NAME,
help="HuggingFace model name (default: config.BERT_MODEL_NAME)")
args = parser.parse_args()
setup_logging()
train_df = pd.read_parquet(cfg.TRAIN_PATH)
val_df = pd.read_parquet(cfg.VAL_PATH)
test_df = pd.read_parquet(cfg.TEST_PATH)
# Baseline 1
if not args.skip_tfidf:
print("=" * 60)
print("Baseline 1: TF-IDF + Logistic Regression (overall 3-class)")
print("=" * 60)
_, m = train_tfidf_baseline(train_df, val_df, test_df)
print(json.dumps({k: v for k, v in m.items() if not k.endswith("_report")},
indent=2))
# Baseline 2
if not args.skip_bert_overall:
print("=" * 60)
print("Baseline 2: BERT fine-tune (overall 3-class)")
print("=" * 60)
train_bert_overall(train_df=train_df, val_df=val_df,
bert_name=args.bert_name,
epochs=args.epochs, batch_size=args.batch_size)
# Baseline 3 — KEY ablation for the paper
if not args.skip_acsa_no_meta:
print("=" * 60)
print("Baseline 3: BERT-ACSA (per-aspect, NO metadata) — ablation")
print("=" * 60)
train_acsa(train_df=train_df, val_df=val_df,
bert_name=args.bert_name,
epochs=args.epochs, batch_size=args.batch_size)
if __name__ == "__main__":
main()
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